Acta Optica Sinica, Volume. 37, Issue 11, 1115005(2017)
Target Scale Adaptive Robust Tracking Based on Fusion of Multilayer Convolutional Features
For the problems about robust tracking and precision scale estimation of the visual objects in the complex tracking conditions, a target scale adaptive robust tracking algorithm based on the fusion of multilayer convolutional features is proposed. First, the multilayer convolutional features are extracted from the target candidate area using VGG-Net-19 deep convolutional network architecture. By constructing the two-dimensional location filters by correlation filtering algorithm and fusing the multilayer convolutional features, the center location of the target is determined. Then, through the multi-scale sampling of target, the histogram of oriented gradient features are extracted to construct the one-dimensional scale filter to achieve the optimal scale estimation. The experimental results show that the proposed algorithm gains the best success rate and precision compared with the six state-of-the-art methods. Meanwhile, this algorithm achieves an adaptive tracking to the fast scale changing of target effectively, and possesses a fast tracking speed.
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Xin Wang, Zhiqiang Hou, Wangsheng Yu, Zefenfen Jin, Xianxiang Qin. Target Scale Adaptive Robust Tracking Based on Fusion of Multilayer Convolutional Features[J]. Acta Optica Sinica, 2017, 37(11): 1115005
Category: Machine Vision
Received: Jun. 21, 2017
Accepted: --
Published Online: Sep. 7, 2018
The Author Email: Wang Xin (wangxiin@foxmail.com)